Statistical Model Research Articles

Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models

Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models

Prenatal exposure to polycyclic aromatic hydrocarbons and phthalate acid esters and gestational diabetes mellitus: A prospective cohort study

BackgroundPolycyclic aromatic hydrocarbons and phthalate acid esters (PAHs & PAEs), known as endocrine disrupting chemicals (EDCs), widely exist in daily life and industrial production. Previous studies have suggested that PAHs & PAEs may modify the intrauterine homeostasis and have adverse effects on fetal development. However, epidemiological evidence on the associations between PAHs & PAEs and gestational diabetes mellitus (GDM) is still limited. ObjectiveTo investigate the effects of prenatal PAHs &PAEs exposure on the risk of GDM and hyperglycemia in pregnant women. MethodsThe study population was a total of 725 pregnant women from a prospective birth cohort study conducted from December 2019 to December 2021. Blood glucose levels were collected by the hospital information system. Urinary PAHs & PAEs concentrations were determined by gas chromatography tandem mass spectrometry. The Poisson regression in a generalized linear model (GLM), multiple linear regression, quantile-based g-computation method (qgcomp), and Bayesian kernel machine regression (BKMR) were applied to explore and verify the individual and overall effects of PAHs & PAEs on glucose homeostasis. Potential confounders were adjusted in all statistical models. ResultsA total of 179 (24.69%) women were diagnosed with GDM. The Poisson regression suggested that a ln-unit increment of 4-OHPHE (4-hydroxyphenanthrene) (adjusted Risk Ratio (aRR) = 1.13; 1.02–1.26) was associated with the increased GDM risk. Mixed-exposure models showed similar results. We additionally found that MBZP (mono-benzyl phthalate) (aRR = 1.19; 1.02–1.39) was positively related to GDM risk in qgcomp model. Although neither model demonstrated that 2-OHNAP (2-hydroxynaphthalene) and 9-OHFLU (9-hydroxyfluorene) increased the risk of GDM, 2-OHNAP and 9-OHFLU exposure significantly increased blood glucose levels. BKMR model further confirmed that overall effects of PAHs & PAEs were significantly associated with the gestational hyperglycemia and GDM risk. ConclusionsOur study presents that environmental exposure to PAHs & PAEs was positively associated with gestational glucose levels and the risks of developing GDM. In particular, 2-OHNAP, 9-OHFLU, 4-OHPHE and MBZP may serve as important surveillance markers to prevent the development of GDM.

Statistical modeling and optimization of heavy metals (Pb and Cd) adsorption from aqueous solution by synthesis of Fe3O4/SiO2/PAM: isotherm, kinetics, and thermodynamic

Statistical modeling and optimization of heavy metals (Pb and Cd) adsorption from aqueous solution by synthesis of Fe3O4/SiO2/PAM: isotherm, kinetics, and thermodynamic

Assessment of Probability of Pulmonary Fibrosis Progression in Patients with Interstitial Lung Diseases to Determine Further Treatment Tactics

The objective: to develop a method for assessing the probability of progression of pulmonary fibrosis in patients with interstitial lung diseases in order to determine further treatment tactics.Subjects and Methods. 80 patients with interstitial lung diseases underwent lung biopsies with consequent morphological and immunohistochemical tests. The group with progressive fibrosis consisted of 42 patients. The prognostic model was built using the binary logistic regression method.Results. A statistical model was developed in the form of a regression equation consisting of 3 parameters: the presence of a morphological pattern of interstitial fibrosis; intensity of CTGF expression; and intensity of TGF-β expression. This mathematical model makes it possible to predict the progression of pulmonary fibrosis in patients with interstitial lung diseases with 83.3% sensitivity and 84.2% specificity.

Evaluation of the Current Situation of Tea Production and Consumption in Bangladesh Through Different Statistical Models

Evaluation of the Current Situation of Tea Production and Consumption in Bangladesh Through Different Statistical Models

Clothing suggestions based on comfort extracted from physiological and emotional parameters

PurposeThe comfort of apparel is not only a feeling of perception but also a tangible measure. The fit and fabric of clothing can exert a perception of comfort for the wearer, whereas actual comfort largely depends on physiological and emotional soothing. However, there is still no solid work on connecting the bridge between physiological and emotional feelings to the comfort of clothing. In this study, we have conceptualized, formulated and proven the relation between physiological and emotional parameters with clothing fit and fabric to find the true comfort of the wearer.Design/methodology/approachA mixed-method research design using physiological and emotional parameters for different fabric and fit combinations were used for this study. The physiological comfort parameters (i.e. heart rate and respiration rate) are extracted from the subjects using gold-standard clinical devices for various fit and fabric combinations. For the emotional response, a survey was conducted for the same subjects wearing all the fit and fabric combinations. Statistical analysis and modeling were performed to obtain the results.FindingsPhysiological indicators such as heart rate are closely linked with user comfort. Due to the limitations in environmental control, the physiological changes obtained did not significantly vary for different fabric and fit combinations of the clothing. However, a significant change in emotional response indicated a definite relationship between different fabric and fit types. Based on the participants’ responses, weather conditions, size of the clothing item, types of fabrics and style also influence the participants’ choice of clothing.Originality/valueThe research was conducted to discover the relation between true comfort (physiological and emotional parameters) and clothing (fit and fabric), which is unique to the field. This study closes the gap and builds up the relationship, which can help introduce clothing comfort to users in the future. The findings of this study help us understand how fabric types (natural or synthetic) and clothing fit types (loose or fitted) can affect physiological and emotional responses, which can provide the consumer with satisfactory clothing with the suitable properties needed.

Water quality level estimation using IoT sensors and probabilistic machine learning model

ABSTRACT Drinking water purity analysis is an essential framework that demands several real-world parameters to ensure the quality of water. So far, sensor-based analysis of water quality in specific environments is done concerning certain parameters including the PH level, hardness, TDS, etc. The outcome of such methods analyzes whether the environment provides potable water or not. Potable denotes the purified water that is free from all contaminations. This analysis gives an absolute solution whereas the demand for drinking water is a growing problem where the multiple-level estimations are essential to use the available water resources efficiently. In this article, we used a benchmark water quality assessment dataset for analysis. To perform a level assessment, we computed three major features namely correlation-entropy, dynamic scaling, and estimation levels, and annexed with the earlier feature vector. The assessment of the available data was performed using the statistical machine learning model that ensembles the random forest model and light gradient boost model (GBM). The probability of the ensemble model was done by the Kullback Libeler Divergence model. The proposed probabilistic model has achieved an accuracy of 96.8%, a sensitivity of 94.55%, and a specificity of 98.29%.

Counteraction of Russian Finance Market Against External Sanctions Pressure

The article shows the impact of external economic sanctions on the Russian financial market. Special attention is paid to the currency, commodity and derivative markets of the Moscow Stock Exchange. The main focus of the work is the analysis of changes in market dynamics and pricing under the influence of political and economic constraints. Methodologically, the study is based on a comprehensive analysis of trade session data, including statistical processing and modeling of market reactions. The use of actual analytical methods made it possible to identify nonlinear dependencies and hidden patterns of behavior of market participants. The results of the study demonstrate that sanctions have had a significant impact on volatility, liquidity and market structure. The authors show the influence of political factors on financial instruments in a crisis, as well as develop new methodological approaches to analyzing financial market data. The results of the work can be used to coordinate trading strategies, develop market regulation policies and assess risks in conditions of political instability. The study also provides a basis for further analysis of the consequences of economic sanctions and the development of strategies to adapt to changing market conditions.

Counteraction of Russian Finance Market Against External Sanctions Pressure

The article shows the impact of external economic sanctions on the Russian financial market. Special attention is paid to the currency, commodity and derivative markets of the Moscow Stock Exchange. The main focus of the work is the analysis of changes in market dynamics and pricing under the influence of political and economic constraints. Methodologically, the study is based on a comprehensive analysis of trade session data, including statistical processing and modeling of market reactions. The use of actual analytical methods made it possible to identify nonlinear dependencies and hidden patterns of behavior of market participants. The results of the study demonstrate that sanctions have had a significant impact on volatility, liquidity and market structure. The authors show the influence of political factors on financial instruments in a crisis, as well as develop new methodological approaches to analyzing financial market data. The results of the work can be used to coordinate trading strategies, develop market regulation policies and assess risks in conditions of political instability. The study also provides a basis for further analysis of the consequences of economic sanctions and the development of strategies to adapt to changing market conditions.

Active Learning for a Recursive Non-Additive Emulator for Multi-Fidelity Computer Experiments

Computer simulations have become essential for analyzing complex systems, but high-fidelity simulations often come with significant computational costs. To tackle this challenge, multi-fidelity computer experiments have emerged as a promising approach that leverages both low-fidelity and high-fidelity simulations, enhancing both the accuracy and efficiency of the analysis. In this paper, we introduce a new and flexible statistical model, the Recursive Non-Additive (RNA) emulator, that integrates the data from multi-fidelity computer experiments. Unlike conventional multi-fidelity emulation approaches that rely on an additive auto-regressive structure, the proposed RNA emulator recursively captures the relationships between multi-fidelity data using Gaussian process priors without making the additive assumption, allowing the model to accommodate more complex data patterns. Importantly, we derive the posterior predictive mean and variance of the emulator, which can be efficiently computed in a closed-form manner, leading to significant improvements in computational efficiency. Additionally, based on this emulator, we introduce four active learning strategies that optimize the balance between accuracy and simulation costs to guide the selection of the fidelity level and input locations for the next simulation run. We demonstrate the effectiveness of the proposed approach in a suite of synthetic examples and a real-world problem. An R package RNAmf for the proposed methodology is provided on CRAN.

Commentary: Optimism for the future of research on disruptive behaviors - an appreciation of good science as illustrated by Nobakht, Steinsbekk & Wichstrom (2023).

This paper by Nobakht, Steinsbekk & Wichstrom (2023) is a model of good science in the study of oppositional defiant disorder and conduct disorder. Their approach illustrates a thoughtful research design, statistical modeling sufficient to empirically evaluate developmental processes, and a full consideration of the theoretical implications of their work. This contrasts with a broad history of research on ODD and CD that far too often has only reified biased assumptions about these phenomena rather than rigorously scrutinizing them. Their demonstration of a unidirectional developmental flow of influence from ODD to interparental aggression, and thence to CD highlights a set of complicated developmental processes involving these disorders and their environment. It expands on evidence of the toll that ODD exerts on parents and provides guidance for more specific intervention. Standards in developmental psychopathology research should include testing bidirectional processes and employing designs that could falsify rather than reify existing beliefs. Examining key mechanisms in such processes will more rapidly generate improvements in assessment and treatment.

Development of Modular Nitrenium Bipolar Electrolytes for Possible Applications in Symmetric Redox Flow Batteries.

Amid the escalating integration of renewable energy sources, the demand for grid energy storage solutions, including non-aqueous organic redox flow batteries (oRFBs), has become ever more pronounced. oRFBs face a primary challenge of irreversible capacity loss attributed to the crossover of redox-active materials between half-cells. A possible solution for the crossover challenge involves utilization of bipolar electrolytes that act as both the catholyte and anolyte. Identifying such molecules poses several challenges as it requires a delicate balance between the stability of both oxidation states and energy density, which is influenced by the separation between the two redox events. We report the development of a diaminotriazolium redox-active core capable of producing two electronically distinct persistent radical species with typically extreme reduction potentials (E1/2red < -2 V, E1/2ox > +1 V, vs Fc0/+) and up to 3.55 V separation between the two redox events. Structure-property optimization studies allowed us to identify factors responsible for fine-tuning of potentials for both redox events, as well as separation between them. Mechanistic studies revealed two primary decomposition pathways for the neutral radical charged species and one for the radical biscation. Additionally, statistical modeling provided evidence for the molecular descriptors to allow identification of the structural features responsible for stability of radical species and to propose more stable analogues.

Synthesis of ZnV2O6 nanosheet photocatalysts for efficient photodegradation of Rhodamine B: Experimental and RSM modeling

A ZnV2O6 photocatalyst with nano-sheet morphology was synthesized using a facile solvothermal approach. To optimize the photo-decomposition of Rhodamine B (RhB), a new surface response methodology (RSM) based on the Central Composite Design (CCD) approach was employed. Several structural/microstructural and spectroscopic characterization techniques were utilized to describe the physical and chemical characteristics of ZnV2O6 (ZVO) nanosheet such as XRD, Raman, SEM-TEM, DRS, PL/TRF. The optimal conditions for RhB photo-decomposition were mathematically discussed as a function of four variables such as pollutant dose, pH values, photocatalyst-weight, and irradiation time, which were modeled by CCD-RSM employing a quadratic statistical model and an optimization approach (ANOVA analysis). RhB degradation efficiency of 97 % was achieved for the optimal conditions of pH = 8, photocatalyst mass = 95 mg, RhB concentration = 9 ppm, and irradiation time = 120. The registered reaction constant was 0.029 min−1. The removal efficiencies of TOC and COD when using the ZVO photo-catalyst for the RhB degradation were 83 % and 88 % respectively. The re-usability of ZnV2O6 samples has been assessed in 4 consecutive series revealing a high stability of the material after extended periods of photo-catalytic reaction. Furthermore, the active radical species involved in the breakdown of RhB were further confirmed through quenching tests in which hydroxyls and holes were found to be the major species contributing to the decomposition of RhB.

P-646 Prognostic statistical model of age-specific anti-Müllerian hormone (AMH) decrease based on hormone values of 23,528 women

Abstract Study question The analysis of anti-Müllerian (AMH) levels, can a useful statistical model be constructed to predict the rate of decrease of AMH levels at different ages? Summary answer A determination of AMH levels can be an objective marker of a ovarian age and a prognostic marker of the rate of ovarian function decline. What is known already The anti-Müllerian hormone is a dimeric glycoprotein produced by granulosa cells of the antral follicles in the ovaries, and AMH participates in the formation of the dominant follicle during folliculogenesis. Serum AMH levels are relatively constant and show little variability between and within the menstrual cycle. The level of serum AMH is proportional to the number of antral follicles and therefore most suitable for determining ovarian reserve. For this reason, AMH is a convenient marker for the biological age of the ovary and for the response to controlled ovarian stimulation (COS). Study design, size, duration A nationwide cross-sectional retrospective study was conducted between 2017 and 2023 and included 23,528 women the ages of 20 - 50, in which AMH levels were measured once. We performed an analysis of the different AMH levels at different ages and constructed a predictive statistical model for the rate of AMH decline. Results were analyzed in subgroups with AMH values &amp;lt; 1 ng/mL, between 1 and 3,5 ng/mL, and &amp;gt; 3,5 ng/mL, at one-year intervals. Participants/materials, setting, methods All women who visited the laboratory for AMH testing were included in the analysis. The samples were collected in 38 points throughout the country and the analysis was carried out in one centralized laboratory using the Ansh Labs (Webster, TX, USA) manual assays, including the ultra-sensitive AMH enzyme-linked immunosorbent assay (ELISA), with a management range of 0,08 - 14,2 ng/ml. The intra-assay variations were 1,97% and 4,00%. The inter-assay variations were 4,63% and 1,98%, respectively. Main results and the role of chance When analyzing all samples in each consecutive year, we found that the rate of AMH decline was approximately linear and inversely proportional to the woman’s age. However, when analyzing the results for the rate of AMH reduction in the different subgroups, we found out that: In women with AMH &amp;lt; 1 ng/ml, the rate of decline with advancing age changes as follows: until age 34, the decline in AMH is very slow, between ages 35 and 45 there is a rapid acceleration of the process.. In women with AMH between 1 and 3,5 ng/ml, the rate of decline with age was approximately constant. In women with AMH &amp;gt; 3,5 ng/ml, the rate of hormone decline is approximately constant but the year-on-year decline rate is decreasing. Based on our analysis and clinical experience, it is our opinion that the rate of AMH decline for the low AMH group has a non-linear characteristic, while ingeneral different groups exhibit different dynamics for the rate of decline. On this basis, we created a statistical model to predict the decrease in AMH levels at different ages. Limitations, reasons for caution Women of only one ethnicity were included in the study. The studied group is heterogeneous, which does not exclude the inclusion of women with a certain pathology and infertility. In some cases, a single determination of AMH could not predict individual differences in rates of hormone reduction. Wider implications of the findings Determination of reference values for AMH at different ages, based on the large volume of examined samples may facilitate the application of AMH in clinical practice. Based on our statistical analysis, creating a prognostic model for decreasing ovarian function and time to menopause appears feasible. Trial registration number NA

Controlled CMS Data Demonstrates a Cost and Clinical Advantage for Hyperbaric Oxygen for Radiation Cystitis

ABSTRACT Feldmeier J, Kirby J, Gelly H, Robins M, Peters J, Gruhn P, Pal S. Controlled CMS Data Demonstrates a Cost and Clinical Advantage for Hyperbaric Oxygen for Radiation Cystitis. Undersea Hyperb Med. 2024 Second Quarter; 51(2):145-157. Introduction: Increasing cancer survivorship, in part due to new radiation treatments, has created a larger population at risk for delayed complications of treatment. Radiation cystitis continues to occur despite targeted radiation techniques. Materials and Methods: To investigate value-based care applying hyperbaric oxygen (HBO2) to treat delayed radiation cystitis, we reviewed public-access Medicare data from 3,309 patients from Oct 1, 2014, through Dec 31, 2019. Using novel statistical modeling, we compared cost and clinical effectiveness in a hyperbaric oxygen group to a control group receiving conventional therapies. Results: Treatment in the hyperbaric group provided a 36% reduction in urinary bleeding, a 78% reduced frequency of blood transfusion for hematuria, a 31% reduction in endoscopic procedures, and fewer hospitalizations when study patients were compared to control. There was a 53% reduction in mortality and reduced unadjusted Medicare costs of $5,059 per patient within the first year after completion of HBO2 treatment per patient. When at least 40 treatments were provided, cost savings per patient increased to $11,548 for the HBO2 study group compared to the control group. This represents a 37% reduction in Medicare spending for the HBO2-treated group. We also validate a dose-response curve effect with a complete course of 40 or more HBO2 treatments having better clinical outcomes than those treated with fewer treatments. Conclusion: These data support previous studies that demonstrate clinical benefits now with cost- effectiveness when adjunctive HBO2 treatments are added to routine interventions. The methodology provides a comparative group selected without bias. It also provides validation of statistical modeling techniques that may be valuable in future analysis, complementary to more traditional methods.

Comparison of Infinium MethylationEPIC v2.0 to v1.0 for human population epigenetics: considerations for addressing EPIC version differences in DNA methylation-based tools.

The recently launched DNA methylation profiling platform, Illumina MethylationEPIC BeadChip Infinium microarray v2.0 (EPICv2), is highly correlated with measurements obtained from its predecessor MethylationEPIC BeadChip Infinium microarray v1.0 (EPICv1). However, the concordance between the two versions in the context of DNA methylation-based tools, including cell type deconvolution algorithms, epigenetic clocks, and inflammation and lifestyle biomarkers has not yet been investigated. We profiled DNA methylation on both EPIC versions using matched venous blood samples from individuals spanning early to late adulthood across three cohorts. On combining the DNA methylomes of the cohorts, we observed that samples primarily clustered by the EPIC version they were measured on. Within each cohort, when we calculated cell type proportions, epigenetic age acceleration (EAA), rate of aging estimates, and biomarker scores for the matched samples on each version, we noted significant differences between EPICv1 and EPICv2 in the majority of these estimates. These differences were not significant, however, when estimates were adjusted for EPIC version or when EAAs were calculated separately for each EPIC version. Our findings indicate that EPIC version differences predominantly explain DNA methylation variation and influence estimates of DNA methylation-based tools, and therefore we recommend caution when combining cohorts run on different versions. We demonstrate the importance of calculating DNA methylation-based estimates separately for each EPIC version or accounting for EPIC version either as a covariate in statistical models or by using version correction algorithms.

Association of frailty with workplace social activity, physical activity, and well-being among older employees: a moderated mediation in two income-variant samples

BackgroundResearch suggests that frailty is associated with lower physical activity and well-being in old age, but social activities at work may facilitate physical activity and its positive effect on well-being among older employees with frailty. This study, therefore, ascertained whether there is a moderated mediation of the association of frailty, Workplace Social Activity (WSA), and well-being by Physical Activity (PA).MethodsThe study adopted a cross-sectional design with relevant sensitivity analyses for confounding. The participants were within two Ghanaian samples with different income levels (low-income, n = 897, and higher income, n = 530). The minimum samples were calculated, and the statistical models were tested with Haye’s Process Model through structural equation modelling.ResultsFrailty was negatively associated with PA, and this relationship was moderated by WSA in both samples. Higher frailty was directly and indirectly associated with lower well-being in the higher-income sample but only indirectly associated with lower well-being in the low-income sample. The mediation of PA in the frailty-well-being relationship is partial in the higher-income sample but complete in the low-income sample. There was evidence of moderated mediation in both samples.ConclusionWSA may reduce the strength of the negative association of frailty with PA and well-being among older employees in both samples. Workplace interventions aimed at enhancing WSA may encourage PA and enhance well-being among older employees with frailty.

Association of urinary metal element with semen quality: a cross-sectional study from Eastern China.

The effect of metallic elements on semen quality remains controversial, with limited evidence on the effects of metal mixtures. We conducted a study involving 338 participants from multiple centers in Eastern China, measuring 17 urinary metals and semen quality parameters. Our analysis used various statistical models, including multivariate logistic and linear regression, Bayesian Kernel Machine Regression, and weighted quantile sum models, to examine the associations between metal levels and semen quality. Logistic regression showed that higher urinary lead was associated with increased risk of abnormal sperm concentration (OR = 1.86, p = 0.021), arsenic to higher abnormal progressive motility risk (OR = 1.49, p = 0.027), and antimony to greater abnormal total motility risk (OR = 1.37, p = 0.018). Conversely, tin was negatively correlated with the risk of abnormal progressive motility (OR = 0.76, p = 0.012) and total motility (OR = 0.74, p = 0.003), respectively. Moreover, the linear models showed an inverse association between barium and sperm count, even after adjusting for other metals (β = - 0.32, p < 0.001). Additionally, the WQS models showed that the metal mixture may increase the risk of abnormal total motility (βWQS = 0.55, p = 0.046). In conclusion, semen quality may be adversely affected by exposure to metals such as arsenic, barium, lead, and antimony. The combined effect of the metal mixture appears to be particularly impaired total motility.

Sources of seasonal water supply forecast uncertainty during snow drought in the Sierra Nevada

AbstractUncertainty attribution in water supply forecasting is crucial to improve forecast skill and increase confidence in seasonal water management planning. We develop a framework to quantify fractional forecast uncertainty and partition it between (1) snowpack quantification methods, (2) variability in post‐forecast precipitation, and (3) runoff model errors. We demonstrate the uncertainty framework with statistical runoff models in the upper Tuolumne and Merced River basins (California, USA) using snow observations at two endmember spatial resolutions: a simple snow pillow index and full‐catchment snow water equivalent (SWE) maps at 50 m resolution from the Airborne Snow Observatories. Bayesian forecast simulations demonstrate a nonlinear decrease in the skill of statistical water supply forecasts during warm snow droughts, when a low fraction of winter precipitation remains as SWE. Forecast skill similarly decreases during dry snow droughts, when winter precipitation is low. During a shift away from snow‐dominance, the uncertainty of forecasts using snow pillow data increases about 1.9 times faster than analogous forecasts using full‐catchment SWE maps in the study area. Replacing the snow pillow index with full‐catchment SWE data reduces statistical forecast uncertainty by 39% on average across all tested climate conditions. Attributing water supply forecast uncertainty to reducible error sources reveals opportunities to improve forecast reliability in a warmer future climate.

OpenHands: An Open-Source Statistical Shape Model of the Finger Bones.

This paper presents statistical shape models of the four fingers of the hand, with an emphasis on anatomic analysis of the proximal and distal interphalangeal joints. A multi-body statistical shape modelling pipeline was implemented on an exemplar training dataset ofcomputed tomography (CT) scansof 10 right hands (5F:5M, 27-37 years, free from disease or injury) imaged at 0.3mm resolution, segmented, meshed and aligned. Model generated included pose neutralisation to remove joint angle variation during imaging. Repositioning was successful; no joint flexion variation was observed in the resulting model. The first principal component (PC) of morphological variation represented phalanx size in all fingers. Subsequent PCs showed variation in position along the palmar-dorsal axis, and bone breadth: length ratio. Finally, the models were interrogated to provide gross measures of bone lengths and joint spaces. These models have been published for open use to support wider community efforts in hand biomechanical analysis, providing bony anatomy descriptions whilst preserving the security of the underlying imaging dataand privacy of the participants. The model describes a small, homogeneous population, and assumptions cannot be made about how it represents individuals outside the training dataset. However, it supplements anthropometric datasets with additional shape information, and may be useful for investigating factors such as joint morphology and design of hand-interfacing devices and products. The model has been shared as an open-source repository ( https://github.com/abel-research/OpenHands ), and we encourage the community to use and contribute to it.